Allergy Wheal and Erythema Segmentation Using Attention U-Net.
The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the nec...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 467 - 476 |
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| Main Authors: | , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471453&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471453 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471453 184471453 184471453 10.1007/s10278-024-01075-0 184471453 ppf: 467 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Allergy Wheal and Erythema Segmentation Using Attention U-Net. aug: au: Lee, Yul Hee Shim, Ji-Su Kim, Young Jae Jeon, Ji Soo Kang, Sung-Yoon Lee, Sang Pyo Lee, Sang Min Kim, Kwang Gi affil: https://ror.org/03ryywt80 Department of Nursing, Gachon University College of Nursing, 191, Hambangmoe-ro, Yeonsu-gu, 21936, Incheon, Korea sug: subj: Deep Learning Image Processing, Computer Assisted Erythema Radiography Skin Tests Hypersensitivity Diagnosis Predictive Value of Tests Image Interpretation, Computer Assisted Human Funding Source Male Female Adult Middle Age Prediction Models Sensitivity and Specificity Descriptive Statistics Erythema Diagnosis Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera. Subsequently, we assessed the deep learning model's performance by comparing the results with ground-truth data. Using contrast-limited adaptive histogram equalization (CLAHE), an image preprocessing technique designed to enhance image contrast, we augmented the chromatic contrast in 46 SPT images from 33 participants. We established a deep learning model for wheal and erythema segmentation using 144 and 150 training datasets, respectively. The wheal segmentation model achieved an accuracy of 0.9985, a sensitivity of 0.5621, a specificity of 0.9995, and a Dice similarity coefficient of 0.7079, whereas the erythema segmentation model achieved an accuracy of 0.9660, a sensitivity of 0.5787, a specificity of 0.97977, and a Dice similarity coefficient of 0.6636. The use of image preprocessing and deep learning technology in SPT is expected to have a significant positive impact on medical practice by ensuring the accurate segmentation of wheals and erythema, producing consistent evaluation results, and simplifying diagnostic processes. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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